基于条件生成对抗网络的咬翼片图像分割  

Bitewing Radiography Image Segmentation Based on Conditional Generative Adversarial Network

作　　者：蒋芸 谭宁 张海 彭婷婷 JIANG Yun;TAN Ning;ZHANG Hai;PENG Tingting(School of Computer Science and Engineering,Northwest Normal University,Lanzhou 730000,China)

机构地区：[1]西北师范大学计算机科学与工程学院,兰州730000

出　　处：《计算机工程》2019年第4期223-227,共5页Computer Engineering

基　　金：国家自然科学基金(61163036);甘肃省自然科学基金(1606RJZA047);甘肃省高校研究生导师科研项目(1201-16);西北师范大学第三期"知识与创新工程"科研骨干项目(nwnu-kjcxgc-03-67)

摘　　要：现有基于U型网络(U-Net)的咬翼片图像分割方法将咬翼片X射线图像分割成龋齿、牙釉质、牙本质、牙髓、牙冠、修复体和牙根管7个部分,但分割准确率偏低。为此,提出一种改进的咬翼片图像分割方法,将条件生成对抗网络与U-Net相结合对咬翼片进行分割,使判别器与生成器相互优化,获得具有更多上下文信息的分割特征图。实验结果表明,改进方法的Dice系数相比U-Net方法提升了0.133,分割准确率更高。The existing bitewing radiography image segmentation method based on U-Net divides the X-ray image of the bitewing radiography into caries,enamel,dentin,pulp,crown,prosthesis and root canal,but the segmentation accuracy is low.So,this paper proposes an improved method to segmentation bitewing radiograpy images.The conditional Generative Adversarial Network(cGAN) combined with U-Net to segmentation the bitewing radiograpy images.It optimizes the discriminator and the generator to obtain a segmentation feature map with more context information.Experimental results show that the Dice coefficient of the improved method is improved by 0.133 compared to the U-Net method, and the segmentation accuracy is higher.

关 键 词：生成对抗网络 图像分割 深度学习 U型网络 数据增强 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]